van Wagensveld Lilian, Walker Cedric, Hahn Kerstin, Sanders Joyce, Kruitwagen Roy, van der Aa Maaike, Sonke Gabe, Rottenberg Sven, Van de Vijver Koen, Janowczyk Andrew, Horlings Hugo
Netherlands Comprehensive Cancer Organization.
University of Bern.
Res Sq. 2023 Nov 14:rs.3.rs-3511087. doi: 10.21203/rs.3.rs-3511087/v1.
Tumor-stroma ratio (TSR) is prognostic in multiple cancers, while its role in high-grade serous ovarian cancer (HGSOC) remains unclear. Despite the prognostic insight gained from genetic profiles and tumor-infiltrating lymphocytes (TILs), the prognostic use of histology slides remains limited, while it enables the identification of tumor characteristics via computational pathology reducing scoring time and costs. To address this, this study aimed to assess TSR's prognostic role in HGSOC and its association with TILs. We additionally developed an algorithm, Ovarian-TSR (OTSR), using deep learning for TSR scoring, comparing it to manual scoring.
340 patients with advanced-stage who underwent primary debulking surgery (PDS) or neo-adjuvant chemotherapy (NACT) with interval debulking (IDS). TSR was assessed in both the most invasive (MI) and whole tumor (WT) regions through manual scoring by pathologists and quantification using OTSR. Patients were categorized as stroma-rich (≥ 50% stroma) or stroma-poor (< 50%). TILs were evaluated via immunohistochemical staining.
In PDS, stroma-rich tumors were significantly associated with a more frequent papillary growth pattern (60% vs 34%), while In NACT stroma-rich tumors had a lower Tumor Regression Grading (TRG 4&5, 21% vs 57%) and increased pleural metastasis (25% vs 16%). Stroma-rich patients had significantly shorter overall and progression-free survival compared to stroma-poor (31 versus 45 months; P < 0.0001, and 15 versus 17 months; P = 0.0008, respectively). Combining stromal percentage and TILs led to three distinct survival groups with good (stroma-poor, high TIL), medium (stroma-rich, high TIL, or; stroma-poor, Low TIL), and poor(stroma-rich, low TIL) survival. These survival groups remained significant in CD8 and CD103 in multivariable analysis (Hazard ratio (HR) = 1.42, 95% Confidence-interval (CI) = 1.02-1.99; HR = 1.49, 95% CI = 1.01-2.18, and HR = 1.48, 95% CI = 1.05-2.08; HR = 2.24, 95% CI = 1.55-3.23, respectively). OTSR was able to recapitulate these results and demonstrated high concordance with expert pathologists (correlation = 0.83).
TSR is an independent prognostic factor for survival assessment in HGSOC. Stroma-rich tumors have a worse prognosis and, in the case of NACT, a higher likelihood of pleural metastasis. OTSR provides a cost and time-efficient way of determining TSR with high reproducibility and reduced inter-observer variability.
肿瘤-基质比（TSR）在多种癌症中具有预后价值，但其在高级别浆液性卵巢癌（HGSOC）中的作用仍不明确。尽管从基因图谱和肿瘤浸润淋巴细胞（TILs）中获得了预后信息，但组织学切片在预后评估中的应用仍然有限，而通过计算病理学能够识别肿瘤特征，从而减少评分时间和成本。为解决这一问题，本研究旨在评估TSR在HGSOC中的预后作用及其与TILs的关联。我们还开发了一种算法，即卵巢TSR（OTSR），利用深度学习进行TSR评分，并将其与手动评分进行比较。
340例晚期患者接受了初次肿瘤细胞减灭术（PDS）或新辅助化疗（NACT）及中间性肿瘤细胞减灭术（IDS）。通过病理学家手动评分和使用OTSR进行定量分析，在最具侵袭性（MI）和整个肿瘤（WT）区域评估TSR。患者被分为富基质（≥50%基质）或贫基质（<50%）。通过免疫组织化学染色评估TILs。
在PDS中，富基质肿瘤与更常见的乳头状生长模式显著相关（60%对34%），而在NACT中，富基质肿瘤的肿瘤消退分级较低（TRG 4&5，21%对57%）且胸膜转移增加（25%对16%）。与贫基质患者相比，富基质患者的总生存期和无进展生存期显著缩短（分别为31个月对45个月；P<0.0001，以及15个月对17个月；P=0.0008）。结合基质百分比和TILs可分为三个不同的生存组，即生存良好（贫基质，高TIL）、中等（富基质，高TIL或贫基质，低TIL）和生存较差（富基质，低TIL）。在多变量分析中，这些生存组在CD8和CD103方面仍然具有显著性（风险比（HR）=1.42，95%置信区间（CI）=1.02-1.99；HR=1.49，95%CI=1.01-2.18，以及HR=1.48，95%CI=1.05-2.08；HR=2.24，95%CI=1.55-3.23）。OTSR能够重现这些结果，并与专家病理学家表现出高度一致性（相关性=0.83）。
TSR是HGSOC生存评估的独立预后因素。富基质肿瘤预后较差，在NACT情况下，胸膜转移的可能性更高。OTSR提供了一种成本效益高且省时的方法来确定TSR，具有高重现性并减少观察者间的变异性。
